Impact of the COVID-19 pandemic on the wellbeing of international fellows training in hematology/oncology at the Princess Margaret Cancer Centre (PMCC).
Bibliographic record
Abstract
11038 Background: The COVID-19 pandemic has led to significant disruptions across all levels of medical training. International fellows in subspecialty training programs are essential members of the frontline physician workforce, who may be facing additional and unique challenges being far away from their home country. We aimed to understand the impact of the pandemic on the wellbeing of current international fellows in the Hematology/Oncology training program. Methods: We conducted an online survey of 52 international fellows at the PMCC from July 6-August 10, 2020. There were 60 questions divided into 4 sections: demographics, wellbeing assessment using the validated Short Warwick Edinburgh Mental Wellbeing Scale (SWEMWBS), fellowship specific questions (personal and professional) and coping strategies using the validated brief COPE scale. Results: Response rate was 46% (n = 24). Relevant demographics include: married (65%), male (54%), age between 31-35 years (48%), have children (48%), and home country from Asia (48%). Mean SWEMWBS score was 21, indicating lower overall wellbeing than the general population (23.6). Compared to pre-COVID-19, many reported a decline in their wellbeing (63%), sense of guilt for not being with their family (45%) or helping their country (41%), stress in personal relationships (26%), fatigue (50%), sleep disorders (38%) and loss of interest in daily activities (38%). Personal events were altered by almost 80% and 20% plans to extend their fellowship. According to the Brief-COPE scale, most fellows used more adaptive coping mechanisms (mean score 39.2) as opposed to maladaptive ones (mean score 21.8). Conclusions: The ongoing COVID-19 pandemic has negatively affected the overall wellbeing of international fellows. Understanding the specific challenges and coping mechanisms of international fellows may help Institutions develop better targeted strategies to promote their overall wellbeing, professional development and high-quality patient care during these unprecedented times.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".